Calculate the pooled estimate of variance

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    Estimate Variance
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Homework Help Overview

The discussion revolves around calculating the pooled estimate of variance in the context of comparing means between two populations, specifically boys and girls. Participants are exploring the implications of sample sizes and variance estimates in hypothesis testing.

Discussion Character

  • Exploratory, Conceptual clarification, Mathematical reasoning, Problem interpretation

Approaches and Questions Raised

  • The original poster attempts to calculate the pooled variance using provided sample variances and sizes, while questioning the relationship between the calculated variance and the original variances. Some participants suggest a simpler approach due to uniform sample sizes, proposing the arithmetic mean of the variances instead. Others raise concerns about the notation used in the hypothesis testing setup.

Discussion Status

Participants are actively engaging with the calculations and interpretations of the hypothesis testing. Some guidance has been offered regarding notation and the logical structure of hypotheses, but there is no explicit consensus on the correctness of the original poster's approach.

Contextual Notes

There is mention of confusion regarding the distinction between population data and sample data, as well as the implications of sample sizes on the calculation methods. The discussion also highlights the need for clarity in notation related to the hypotheses being tested.

chwala
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Homework Statement
See attached
Relevant Equations
stats
1648466908376.png

OK, Let me attempt part (i), first,
Here we have;
##s^2_p ##=##\dfrac{ (n_1-1)s_1^2+(n_2-1)s_2^2}{n_1+n_2-2}##

##s^2_p ##=##\dfrac{ (7-1)0.63953+(7-1)0.6148}{7+7-2}##

##s^2_p ##=##\dfrac{3.83718+3.6888}{12}##

##s^2_p ##=##\dfrac{3.83718+3.6888}{12}##

##s^2_p ##=##\dfrac{7.52598}{12}##

##s^2_p ##=##0.627165## its located between the two original variances... correct? i do not have markscheme nor solutions...

* Reading on this topic now...the literature is really confusing on the so called population data (presumably all the n values in a given data set) and sample data...
 
Last edited:
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i) Your sample sizes are uniform, so there's no need for weighted averages here. Find the variance estimates for both populations and then take their arithmetic mean.
 
nuuskur said:
i) Your sample sizes are uniform, so there's no need for weighted averages here. Find the variance estimates for both populations and then take their arithmetic mean.
Ok mate i.e ##\dfrac {0.63953+0.6148}{2}##= ##\dfrac {1.25433}{2}=0.627165## .

For part (ii), Let, ##μ_1## and##μ_2## be the mean for boys and girls respectively, then
We want to test the hypothesis; as per the question...

##H_0##: ##μ_1##=##μ_2## - Mean weight of boys is equal to mean weight of girls.
##H_A##: ##μ_1##<##μ_2## - Mean weight of boys is less than the mean weight of girls.

Using the t-statistic and also considering that dof =##12##and ##α=0.05## then it follows that the critical value = ##-1.782##
We shall therefore have,
##t##=##\dfrac {2.5429-3.18571}{\sqrt{(0.627165^2(\frac {1}{6}+\frac {1}{6})}}##=##\dfrac {2.5429-3.18571}{0.256040263}##=##-2.5105 < -1.782 ## We thefore Reject the Null hypothesis and accept the Alternative hypothesis.
 
Last edited:
Your notation is overloaded. You likely mean ##\mu _1## for boys and ##\mu _2## for girls. Hypotheses are logical negations of one another. This is not the case right now.
 
nuuskur said:
Your notation is overloaded. You likely mean ##\mu _1## for boys and ##\mu _2## for girls. Hypotheses are logical negations of one another. This is not the case right now.
Amended ...sorry was a bit busy...
 
nuuskur said:
Your notation is overloaded. You likely mean ##\mu _1## for boys and ##\mu _2## for girls. Hypotheses are logical negations of one another. This is not the case right now.
Is it now correct?
 

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